Three former DeepMind employees are trying to train a machine to spot and invest in company stocks and cryptocurrencies before they rise. Martin Schmid, Rudolf Kadlec and Matej Moravcik left Alphabet-owned DeepMind in January to set up EquiLibre Technologies, relocating from Edmonton in Canada to Prague in the Czech Republic in the process. The trio all used to work at IBM and in 2017 they developed an AI called DeepStack. It became the first AI capable of beating professional poker players at heads-up no-limit Texas hold'em poker. Now they're looking to apply some of these concepts to financial markets.

An artificial intelligence tool that reads chest X-rays without oversight from a radiologist got regulatory clearance in the European Union last week -- a first for a fully autonomous medical imaging AI, the company, called Oxipit, said in a statement. It's a big milestone for AI and likely to be contentious, as radiologists have spent the last few years pushing back on efforts to fully automate parts of their job. The tool, called ChestLink, scans chest X-rays and automatically sends patient reports on those that it sees as totally healthy, with no abnormalities. Any images that the tool flags as having a potential problem are sent to a radiologist for review. Most X-rays in primary care don't have any problems, so automating the process for those scans could cut down on radiologists' workloads, the Oxipit said in informational materials.

Car manufacturers are facing some of the biggest changes their sector has seen, with the shift to electric vehicles, the development of self-driving cars and a potential threat from electric air taxis that may one day replace some of today's car journeys across congested cities. Partnerships with startups are a good way for carmakers to make sure they can get expertise in these emerging areas, and Sifted was interested in looking at how the different car brands compare in their willingness to invest in startups in strategic areas. What emerges is a picture of European carmakers at the middle to bottom of the pack in terms of the number of startups they have invested in. The one exception is Mercedes-Benz, which has a portfolio of 42 startup investments, second only to Hyundai. Mercedez-Benz's investments are across the board, from a holding in delivery robot company Starship to flying taxi company Volocopter, which looks like it may be one of the first to get passenger services up and running, starting with demo flights at the Paris Olympics in 2024. One area where there is no notable startup investment from Daimler is hydrogen-fuelled vehicles.

Brush clamped firmly in bionic hand, Ai-Da's robotic arm moves slowly, dipping in to a paint palette then making slow, deliberate strokes across the paper in front of her. This, according to Aidan Meller, the creator of the world's first ultra-realistic humanoid robot, Ai-Da, is "mind-blowing" and "groundbreaking" stuff. In a small room at London's British Library, Ai-Da – assigned the she/her pronoun – has become the first robot to paint as artists have painted for centuries. Camera eyes fixed on her subject, AI algorithms prompt Ai-Da to interrogate, select, decision-make and, ultimately, create a painting. It's painstaking work, taking more than five hours a painting, but with no two works exactly the same.

Champion bridge player Sharon Osberg once wrote, "Playing bridge is like running a business. While it's little surprise chess fell to number-crunching supercomputers long ago, you'd expect humans to maintain a more unassailable advantage in bridge, a game of incomplete information, cooperation, and sly communication. Over millennia, our brains have evolved to read subtle facial queues and body language. We've assembled sprawling societies dependent on the competition and cooperation of millions. Surely such skills are beyond the reach of machines? In recent years, the most advanced AI has begun encroaching on some of our most proudly held territory; the ability to navigate an uncertain world where information is limited, the game is infinitely nuanced, and no one succeeds alone. Last week, French startup NukkAI took another step when its NooK bridge-playing AI outplayed eight bridge world champions in a competition held in Paris. The game was simplified, and NooK didn't exactly go ...

To doctors, pajama time means homework. In fact, it's a common phrase describing the nighttime ritual of finishing up clinical notes about the patients they saw earlier that day. As demands for notes and data to chronicle patient interactions from hospital administration and insurance industry payers have increased, the amount of time physicians spend on the computer has squeezed their already tight schedules. A 2017 study published in Annals of Family Medicine found that primary care physicians spend nearly six hours a day interacting with their electronic health records systems during and after clinic hours. Amid pandemic burnout, the stress is enough for doctors to hand over the work of writing clinical notes to an AI-based tool, even if it could create patient data privacy risks and medical errors.

Could artificial intelligence (AI) assessment have comparable diagnostic accuracy to clinician assessment for fracture detection? In a recently published meta-analysis of 42 studies, the study authors noted 92 percent sensitivity and 91 percent specificity for AI in comparison to 91 percent sensitivity and 92 percent specificity for clinicians based on internal validation test sets. For the external validation test sets, clinicians had 94 percent specificity and sensitivity in comparison to 91 percent specificity and sensitivity for AI, according to the study. In essence, the study authors found no statistically significant differences between AI and clinician diagnosis of fractures. "The results from this meta-analysis cautiously suggest that AI is noninferior to clinicians in terms of diagnostic performance in fracture detection, showing promise as a useful diagnostic tool," wrote Dominic Furniss, DM, MA, MBBCh, FRCS(Plast), a professor of plastic and reconstructive surgery in the Nuffield Department of Orthopedics, Rheumatology and Musculoskeletal Sciences at the Botnar Research Centre in Oxford, United Kingdom., and colleagues.

John Quackenbush was frustrated with Google. It was January 2020, and a team led by researchers from Google Health had just published a study in Nature about an artificial intelligence (AI) system they had developed to analyze mammograms for signs of breast cancer. The system didn't just work, according to the study, it worked exceptionally well. When the team fed it two large sets of images to analyze--one from the UK and one from the U.S.--it reduced false positives by 1.2 and 5.7 percent and false negatives by 2.7 and 9.4 percent compared with the original determinations made by medical professionals. In a separate test that pitted the AI system against six board-certified radiologists in analyzing nearly 500 mammograms, the algorithm outperformed each of the specialists. The authors concluded that the system was "capable of surpassing human experts in breast cancer prediction" and ready for clinical trials. An avalanche of buzzy headlines soon followed. "Google AI system can beat doctors at detecting breast cancer," a CNN story declared.

Chemical engineers and materials scientists are constantly looking for the next revolutionary material, chemical, and drug. The rise of machine-learning approaches is expediting the discovery process, which could otherwise take years. "Ideally, the goal is to train a machine-learning model on a few existing chemical samples and then allow it to produce as many manufacturable molecules of the same class as possible, with predictable physical properties," says Wojciech Matusik, professor of electrical engineering and computer science at MIT. "If you have all these components, you can build new molecules with optimal properties, and you also know how to synthesize them. That's the overall vision that people in that space want to achieve" However, current techniques, mainly deep learning, require extensive datasets for training models, and many class-specific chemical datasets contain a handful of example compounds, limiting their ability to generalize and generate physical molecules that could be created in the real world. Now, a new paper from researchers at MIT and IBM tackles this problem using a generative graph model to build new synthesizable molecules within the same chemical class as their training data.

The robot is still 5 to 10 years away from showing up in a clinical setting, researchers said, but the device comes on the heels of a fleet of other robotic innovations allowing doctors the ability to better scan a patient's lungs for cancerous tissue. They are designed to ease a task doctors have long struggled with: reaching the inner recesses of the human body, for diagnostic and treatment purposes, without causing damage or using invasive procedures.